Cyces is an engineering-first product studio with strong expertise in data engineering and cloud operations. The company runs a ride-sharing platform across Southeast Asia that handles thousands of real-time trip events daily. As data volumes grew, Cyces required a modern streaming pipeline to deliver instant insights, support accurate pricing, and improve route decisions, supported by Geopits.

Business Challenges

Batch processing delays

Legacy batch ETL processes created 12-to-24-hour delays in accessing analytics.

High operational overhead

On-prem ETL servers carry significant operational overhead.

Streaming data complexity

Continuous streaming data from IoT devices was difficult to handle at scale.

Peak traffic scalability

Limited scalability caused increased delays during peak traffic hours.

Project Objectives

Geopits partnered with Cyces to replace batch processing with a real-time streaming pipeline built for scale.

Key Goals:

Cut data latency from hours to seconds
Reduce operational overhead through serverless architecture
Build a pipeline that scales with peak ride-sharing traffic

Solution Provided by Geopits

Geopits built a real-time streaming pipeline using Pub/Sub, Dataflow, and Big Query, with windowing and late data handling for accuracy.

Real-time streaming pipeline

Pub/Sub, Dataflow, and Big Query were connected into a single pipeline, with windowing and late data handling for accuracy.

Data privacy and access controls

Cloud DLP is integrated to mask sensitive user and location data, with IAM and VPC Service Controls securing access.

Auto-scaling infrastructure

Auto-scaling was enabled to absorb peak traffic without manual intervention.

Query performance optimization

Big Query partitioning and materialized views were applied for faster analytics.

Results & Business Impact

The shift to a real-time streaming pipeline transformed how Cyces accesses data, at a fraction of the previous operational cost.

Key outcomes

Real-Time Data Processing

Streaming through Google Cloud Pub/Sub, Dataflow, and BigQuery reduced data latency from 24 hours to under 1 minute, providing real-time visibility into trips, pricing, and routing.

99.98% Platform Uptime

An auto-scaling cloud infrastructure maintained 99.98% uptime, ensuring reliable data processing even during peak ride-sharing demand.

40% Lower Operational Overhead

Migrating to serverless Google Cloud services reduced operational overhead by 40% compared to the previous on-premises ETL environment.

Scalable Event Processing

The modern streaming pipeline scales to process millions of trip events per hour without requiring additional operational effort, supporting future business growth.

Conclusion

Geopits partnered with Cyces to replace a 24-hour batch ETL process with a real-time streaming pipeline on Google Cloud, reducing data latency to under 1 minute while improving uptime, scalability, and operational efficiency.

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